搜索结果: 1-13 共查到“K-means Clustering”相关记录13条 . 查询时间(0.093 秒)
MULTI-PATCHES IRIS BASED PERSON AUTHENTICATION SYSTEM USING PARTICLE SWARM OPTIMIZATION AND FUZZY C-MEANS CLUSTERING
Particle swarm optimization Fuzzy c-means Taylor’s series expansion weighted mean Hamming distance Iris recognition system
2017/6/19
Locating the boundary parameters of pupil and iris and segmenting the noise free iris portion are the most challenging phases of an automated iris recognition system. In this paper, we have presented ...
Evaluation of forest fire risk using the Apriori algorithm and fuzzy c-means clustering
wildfire association rules fuzzification Ilam Province Iran
2018/3/6
In this study we evaluated forest fire risk in the west of Iran using the Apriori algorithm and fuzzy c-means (FCM)
clustering. We used twelve different input parameters to model fire risk in Ilam Pr...
Hybrid Anomaly Detection using K-Means Clustering in Wireless Sensor Networks
Hybrid Anomaly Misdirection Blackhole
2016/1/7
Security is the biggest concern in Wireless Sensor Networks (WSNs)
especially for the ones which are deployed for military applications and
monitoring. They are prone to various attacks which degrad...
LIDAR Data Classification Using Hierarchical K-Means Clustering
Remote Sensing LIDAR Hierarchical Classification DTM Multiresolution
2015/12/8
This paper deals with lidar point cloud filtering and classification for modelling the Terrain and more generally for scene segmentation. In this study, we propose to use the well-known K-means cluste...
Performance analysis of EM-MPM and K-means clustering in 3D ultrasound breast image segmentation
segmentation EM-MPM ultrasound
2015/1/20
Mammographic density is an important risk factor for breast cancer, detecting and screening at an early stage could help save lives. To analyze breast density distribution, a good segmentation algorit...
AN EFFICIENT INITIALIZATION METHOD FOR K-MEANS CLUSTERING OF HYPERSPECTRAL DATA
K-means Clustering Initialization methods Unmixing MVES Hyperspectral data
2014/12/1
K-means is definitely the most frequently used partitional clustering algorithm in the remote sensing community. Unfortunately due to its gradient decent nature, this algorithm is highly sensitive to ...
Unsupervised change detection in satellite images using fuzzy c-means clustering and principal component analysis
Change Detection principal component analysis fuzzy c-means clustering image differencing remote sensing bi-temporal satellite images
2014/4/22
Change detection analyze means that according to observations made in different times, the process of defining the change detection occurring in nature or in the state of any objects or the ability of...
AUTOMATIC EXTRACTION OF ROCK JOINTS FROM LASER SCANNED DATA BY MOVING LEAST SQUARES METHOD AND FUZZY K-MEANS CLUSTERING
terrestrial laser scanning rock joint orientation moving least squares fuzzy K-means clustering
2014/6/5
Recent development of laser scanning device increased the capability of representing rock outcrop in a very high resolution. Accurate 3D point cloud model with rock joint information can help geologis...
基于改进型FCM算法的牛肉大理石花纹提取方法（Beef Marbling Extraction Based on Modified Fuzzy C-means Clustering Algorithm）
牛肉 大理石花纹 提取 模糊C均值
2010/12/29
提出了一种基于改进型模糊C均值聚类算法的牛肉大理石花纹提取方法。该方法结合了快速模糊C均值（FCM）聚类算法，对传统FCM算法中的隶属函数、聚类数C和初始聚类中心点选取方法进行了优化。试验表明，该方法使牛肉大理石花纹提取的准确度由76.2%提高到85.7%。
Fuzzy C-means Clustering for 3D Seismic Parameters Processing
3D seismic parameters Structure and growth history Fuzzy C-means clustering
2009/12/3
3D seismic parameters can reflect the features of petroleum reservoir from different profiles. By analizing the3D seismic parameters, we can assess the parameters of the reservoir characterization, su...
基于PSO与K-均值算法的农业超绿图像分割方法（Agriculture Extra-green Image Segmentation Based on Particle Swarm Optimization and K-means Clustering）
图像分割 微粒群算法 K均值算法
2009/9/11
为了解决K-均值算法对农业图像中常用的超绿特征2G—R—B图像分割效果不佳的缺点，提出一种基于微粒群与K均值算法的图像分割方法。先用K均值算法对图像进行快速分类，然后将分类结果作为其中一个微粒的结果，利用微粒群算法计算，最后用K-均值算法在新的分类基础上计算新的聚类中心，更新当前的位置，以得到最优的图像分割阈值。试验结果表明，改进算法对超绿特征2G—R—B图像能够准确分割目标，且对不同类型的农业超...
基于核K—均值聚类算法的植物叶部病害识别（Plant Leaf Disease Recognition Based on Kernel K-means Clustering Algorithm）
植物病害 病害识别 核K—均值聚类
2009/5/22
针对植物叶部病害图像的特点，首先对采集到的玉米病害彩色图像采用矢量中值滤波法去除噪声，然后提取玉米病叶彩色图像的纹理特征和颜色特征作为特征向量，利用Mercer核，把输入空间的样本映射到高维特征空间进行K—均值聚类以及植物病害识别。试验涉及的4种玉米病害识别正确率达82.5%，核K—均值聚类方法适合玉米叶部病害分类。
The k-Means Clustering problem is one of the most-explored problems
in data mining to date. With the advent of protocols that have
proven to be successful in performing single database clustering, t...